Modern Department of Defense mission systems are very complex and therefore arduous to defend, especially in the cyber domain. A major cause for this concern arises from the fact that implementation of security protections occur at a local scale, while the important operational security issues stem from a global perspective of the system, e.g., mission assurance. Being able to understand network-wide implications of local cyber protections has the potential to significantly impact the strategies we use to protect modern mission systems. In this work, we present a graph-theoretic perspective on this problem, which is based on a framework for modeling and assessing the integrated cyber-physical dynamics of complex systems. Under the framework, these dynamics (and their relationships) are modeled as a graph and then analyzed using processing techniques from graphtheory. We demonstrate the utility of this framework by conducting insider-attack threat analysis and show how the application of security protections at a local scale impact network-wide security properties from an insider perspective. As a test case, we study the problem of search and rescue (SAR) using unmanned aerial vehicle teams. Unmanned vehicle teams engaged in SAR are prototypical cyber-physical systems, in which local intrusions may cause global disruptions. Here, we describe how the insider modeling framework for cyber-physical dynamics applies to this problem and present results of a network-wide assessment of security properties of the system. We use this assessment to design a security protection for the system in which we use cryptographically secure computation techniques to limit the amount of information sharing required between system components without degrading the correct operation of the system. We show how the application of these techniques on a local scale impacts the security properties of the system on a global scale.
This article envisions surveillance and estimation in a future data-rich space environment, wherein spacecraft and other systems sense and record myriad environmental parameters incidentally to their primary missions. In this future environment, a wide range of sensor data will be available, but much of the data may be incidental, and hence subject to fluctuation, gaps, and low fidelity. Here, we explore estimation using such incidental-measurement data streams. Specifically, two canonical incidental-measurement-based estimation problems are posed—one concerned with recovering diffusive processes from an incidentally-mobile sensor, the other concerned with object (target) tracking using incidental measurements. Basic formal analyses of these estimation problems are pursued, and simulation results are also presented.